Push sharded checkpoint to hub when push_to_hub=True in `TrainingAr…

…guments` (huggingface#31808)

Save sharded checkpoint in Trainer

src/transformers/trainer.py

@@ -22,6 +22,7 @@
 import glob
 import importlib.metadata
 import inspect
+import json
 import math
 import os
 import random
@@ -4215,6 +4216,15 @@ def _push_from_checkpoint(self, checkpoint_folder):
         output_dir = self.args.output_dir
         # To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder
         modeling_files = [CONFIG_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_NAME]
+        #  Add sharded checkpoints if we have an index
+        for index_file in [WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME]:
+            index_path = os.path.join(checkpoint_folder, index_file)
+            if os.path.isfile(index_path):
+                modeling_files.append(index_file)
+                with open(index_path) as f:
+                    index = json.loads(f.read())
+                shard_files = list(set(index["weight_map"].values()))
+                modeling_files.extend(shard_files)
         if is_peft_available():
             modeling_files.extend([ADAPTER_CONFIG_NAME, ADAPTER_WEIGHTS_NAME, ADAPTER_SAFE_WEIGHTS_NAME])
         for modeling_file in modeling_files: